CN105653845A - Method and device for obtaining triphase relative permeability curve - Google Patents

Abstract

The invention discloses a method and a device for obtaining a triphase relative permeability curve. The method comprises the following steps: on the basis of a gas-water alternant immiscible phase displacement experiment of a target area, obtaining displacement pressure differences, moisture contents and gas-oil ratios at different moments, and forming a dynamic data observed value vector; on the basis of a priori triphase relative permeability curve represented by a constructed representation model, and a constructed component numerical value model, obtaining a current dynamic data predication value vector; utilizing the dynamic data observed value vector and the dynamic data predication value vector to carry out iterative computations on the constructed target function until the iterative computations of the target function meet a preset iterative condition of convergence; and according to the target function which meets the iterative condition of convergence, obtaining an oil-gas-water triphase relative permeability curve of the target area. Through the technical scheme disclosed by the invention, the triphase relative permeability curve suitable for a gas-water alternant immiscible phase flooding technology can be obtained.

Description

A kind of method obtaining three-phase relative permeability curve and device

Technical field

The application relates to Reservoir Development technical field, particularly to a kind of method obtaining oil-gas-water three-phase relative permeability curve and device.

Background technology

Permeability saturation curve is a significant data in Reservoir Development, and it can multiphase porous flow feature in fine description pore media. At present, oil-water or vapor-liquid two phases permeability saturation curve obtain mainly through rock core displacement test, and computational methods are mainly based on analytic method and the Method for Numerical Inversions such as Johnson-Bossler-Naumann (JBN). And the research of the method obtaining oil-gas-water three-phase relative permeability curve is relatively fewer.

Along with the aggravation of oil reservoir longitudinal direction and plain heterogeneity contradiction, China's waterflooding oil field main body generally enters " double; two high " development phase of High water cut, high recovery percent of reserves, and remaining oil presents the Distribution Pattern of " whole height dispersion, Local Phase are to enrichment ". Laboratory experiment and mining site practice have shown that, it is the effective technology water-drive pool later stage improving oil recovery factor further that air-water replaces non-phase-mixing driving. Therefore, research is applicable to the method for the acquisition oil-gas-water three-phase relative permeability curve that air-water replaces non-phase-mixing driving oil tech and seems significant, and this can replace the multiphase porous flow feature offer theoretical foundation of immiscible displacement for air-water in accurate description pore media.

Summary of the invention

The purpose of the embodiment of the present application is to provide a kind of method obtaining three-phase relative permeability curve and device, to realize obtaining the purpose being applicable to the oil-gas-water three-phase relative permeability curve that air-water replaces non-phase-mixing driving oil tech.

For solving above-mentioned technical problem, the embodiment of the present application provides a kind of method obtaining three-phase relative permeability curve and device to be achieved in that

The embodiment of the present application provides a kind of method obtaining three-phase relative permeability curve, including:

S1, the air-water in based target region replaces immiscible displacement experiment, obtains not displacement pressure reduction in the same time, moisture content and gas-oil ratio, and described not displacement pressure reduction in the same time, moisture content and gas-oil ratio constitute dynamic data observation vector;

S2, the priori three-phase relative permeability curve characterized based on constructed characterization model and constructed component numerical simulator, obtain current dynamic data predictive value vector;

S3, utilizes described dynamic data observation vector and described current dynamic data predictive value vector, is iterated constructed object function calculating, until the iterative computation of described object function meets default iteration convergence condition;

S4, according to the object function meeting described iteration convergence condition, obtains the oil-gas-water three-phase relative permeability curve of described target area.

In at least one embodiment, described component numerical simulator builds based on determined physical properties of fluids feature distribution parameter, and described physical properties of fluids parameter attribute includes the Changing Pattern of saturation pressure, oil density, viscosity and volume factor.

In at least one embodiment, described step S2 includes:

S21, obtains priori and controls parameter vector set, and described priori controls parameter vector set and includes the priori control parameter vector corresponding to multiple relative permeability prior model;

S22, based on constructed component numerical simulator, utilizes described priori to control parameter vector set and carries out component numerical simulation, to obtain the priori dynamic data predictive value vector corresponding to the plurality of relative permeability prior model that current time walks;

S23, calculates that each described priori dynamic data predictive value is vectorial and difference between described dynamic data observation vector or error sum of squares, to obtain meeting the Corrective control parameter vector set of preset requirement;

S24, calculates the meansigma methods that the priori dynamic data predictive value corresponding to described Corrective control parameter vector set is vectorial, to obtain current dynamic data predictive value vector,

Accordingly, described step S3 includes:

S31, utilizes described dynamic data observation vector and described current dynamic data predictive value vector, calculates the current value of described object function;

S32, it is judged that current iteration calculates whether meet iteration convergence condition;

S33, when judging that current iteration calculating is unsatisfactory for iteration convergence condition, controls parameter vector set using described Corrective control parameter vector set as priori, repeats step S22-S24, obtain current dynamic data predictive value vector;

S34, after obtaining the current dynamic data predictive value vector corresponding to described Corrective control parameter vector set, repeats step S31-32, until being met the object function of described iteration convergence condition.

In at least one embodiment, the expression formula of constructed object function is as follows:

$\begin{array}{c}O\left(m\right)=\frac{1}{2}{\left(g\right(m)-d_{obs})}^T{C}_D^{-1}\left(g\right(m)-d_{obs})\\ +\frac{1}{2}{(m-m_{prior})}^T{C}_m^{-1}(m-m_{prior})\end{array}$

Wherein, O (m) is object function; M is the control parameter vector in described characterization model; T is the symbol characterizing vector or matrix transpose; d_obsFor dynamic data observation vector; G (m) is dynamic data predictive value vector; C_DCovariance matrix for dynamic data measurement error; m_priorMeansigma methods for the prior model information of described control parameter vector m; C_mCovariance matrix for prior model.

In at least one embodiment, described basis meets the object function of described iteration convergence condition, and the oil-gas-water three-phase relative permeability curve obtaining described target area includes:

According to the object function meeting described iteration convergence condition, it is determined that go out each current value controlling parameter in described control parameter vector;

According to current value controlling parameter each in described control parameter vector, calculate gas phase relative permeability and the oil relative permeability of the oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system;

Utilize gas phase relative permeability and the oil relative permeability of the computed oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system, calculate oil relative permeability when three phase fluid flow.

In at least one embodiment, calculate oil relative permeability when three phase fluid flow by below equation:

K_ro=(K_row+K_rw)(K_rog+K_rg)-(K_rw+K_rg)

In formula, K_roOil relative permeability when for three phase fluid flow; K_rowOil relative permeability for oil-aqueous two phase system; K_rwAqueous phase relative permeability for oil-aqueous two phase system; K_rogOil relative permeability for oil-gas binary system; K_rgGas phase relative permeability for oil-gas binary system.

The embodiment of the present application additionally provides a kind of device obtaining three-phase relative permeability curve, including:

First acquiring unit, air-water for based target region replaces immiscible displacement experiment, obtaining not displacement pressure reduction in the same time, moisture content and gas-oil ratio, described not displacement pressure reduction in the same time, moisture content and gas-oil ratio constitute dynamic data observation vector;

Second acquisition unit, for the priori three-phase relative permeability curve characterized based on constructed characterization model and constructed component numerical simulator, obtains current dynamic data predictive value vector;

Iterative computation unit, is used for utilizing described dynamic data observation vector and described current dynamic data predictive value vector, is iterated constructed object function calculating, until the iterative computation of described object function meets default iteration convergence condition;

3rd acquiring unit, for according to the object function meeting described iteration convergence condition, obtaining the oil-gas-water three-phase relative permeability curve of described target area.

In at least one embodiment, described second acquisition unit includes:

Obtaining subelement, be used for obtaining priori and control parameter vector set, described priori controls parameter vector set and includes the priori control parameter vector corresponding to multiple relative permeability prior model;

Component numerical value analog submodule unit, for based on constructed component numerical simulator, utilize described priori to control parameter vector set and carry out component numerical simulation, to obtain the priori dynamic data predictive value vector corresponding to the plurality of relative permeability prior model that current time walks;

First computation subunit, for calculating that each described priori dynamic data predictive value is vectorial and difference between described dynamic data observation vector or error sum of squares, to obtain Corrective control parameter vector set;

Second computation subunit, for calculating the meansigma methods of the priori dynamic data predictive value vector corresponding to described Corrective control parameter vector set, to obtain current dynamic data predictive value vector,

Accordingly, iterative computation unit includes:

3rd computation subunit, is used for utilizing described dynamic data observation vector and described current dynamic data predictive value vector, calculates the current value of described object function;

Judgment sub-unit, is used for judging current iteration calculates whether meet iteration convergence condition;

First controls subelement, for when judging that current iteration calculating is unsatisfactory for iteration convergence condition, described Corrective control parameter vector set is controlled parameter vector set as priori, and control described component numerical value analog submodule unit, described first computation subunit and described second computation subunit operate accordingly, namely described component numerical value analog submodule unit is controlled based on constructed component numerical simulator, utilize described priori to control parameter vector set and carry out component numerical simulation, to obtain the priori dynamic data predictive value vector corresponding to the plurality of relative permeability prior model that current time walks, control described first computation subunit and calculate that each described priori dynamic data predictive value is vectorial and difference between described dynamic data observation vector, to obtain Corrective control parameter vector set,And control the meansigma methods that described second computation subunit calculating priori dynamic data predictive value corresponding to described Corrective control parameter vector set is vectorial, to obtain the operations such as current dynamic data predictive value vector;

Second controls subelement, for controlling the operation of described 3rd computation subunit and described judgment sub-unit, until being met the object function of iteration convergence condition.

In at least one embodiment, the expression formula of object function constructed in described iterative computation unit is as follows:

$\begin{array}{c}O\left(m\right)=\frac{1}{2}{\left(g\right(m)-d_{obs})}^T{C}_D^{-1}\left(g\right(m)-d_{obs})\\ +\frac{1}{2}{(m-m_{prior})}^T{C}_m^{-1}(m-m_{prior})\end{array}$

Wherein, O (m) is object function; M is the control parameter vector in described characterization model; T is the symbol characterizing vector or matrix transpose; d_obsFor dynamic data observation vector; G (m) is dynamic data predictive value vector; C_DCovariance matrix for dynamic data measurement error; m_priorMeansigma methods for the prior model information of described control parameter vector m; C_mCovariance matrix for prior model.

In at least one embodiment, described 3rd acquiring unit includes:

Determine subelement, described in basis, meet the object function of iteration convergence condition, it is determined that go out each current value controlling parameter in described control parameter vector;

4th computation subunit, for according to current value controlling parameter each in described control parameter vector, calculating gas phase relative permeability and the oil relative permeability of the oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system;

5th computation subunit, for utilizing gas phase relative permeability and the oil relative permeability of the computed oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system, calculate oil relative permeability when three phase fluid flow.

The technical scheme provided from above the embodiment of the present application, the embodiment of the present application replaces immiscible displacement experiment by the air-water in based target region, obtaining not displacement pressure reduction in the same time, moisture content and gas-oil ratio, described not displacement pressure reduction in the same time, moisture content and gas-oil ratio constitute dynamic data observation vector; The priori three-phase relative permeability curve characterized based on constructed characterization model and constructed component numerical simulator, obtain current dynamic data predictive value vector; Utilize described dynamic data observation vector and described current dynamic data predictive value vector, be iterated constructed object function calculating, until the iterative computation of described object function meets default iteration convergence condition; According to the object function meeting described iteration convergence condition, obtain the oil-gas-water three-phase relative permeability curve of target area, it is achieved thereby that obtain the purpose being applicable to the oil-gas-water three-phase relative permeability curve that air-water replaces non-phase-mixing driving oil tech.

Accompanying drawing explanation

In order to be illustrated more clearly that the embodiment of the present application or technical scheme of the prior art, the accompanying drawing used required in embodiment or description of the prior art will be briefly described below, apparently, the accompanying drawing that the following describes is only some embodiments recorded in the application, for those of ordinary skill in the art, under the premise not paying creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.

Fig. 1 is the flow chart of the method obtaining three-phase relative permeability curve that the embodiment of the present application provides;

Fig. 2 is the sub-step flow chart included by step S2;

Fig. 3 is the fitting result chart of displacement pressure reduction and moisture content;

Fig. 4 is the fitting result chart of cumulative produced GOR;

Fig. 5 is the sub-step flow chart included by step S3;

Fig. 6 is oil relative permeability curve and the aqueous phase permeability saturation curve of oil-aqueous two phase system;

Fig. 7 is oil relative permeability curve and the gas phase permeability saturation curve of oil-gas binary system;

Fig. 8 is oil relative permeability curve when three phase fluid flow.

Fig. 9 is the module diagram of a kind of device obtaining three-phase relative permeability curve.

Detailed description of the invention

The embodiment of the present application provides a kind of method obtaining three-phase relative permeability curve and device.

In order to make those skilled in the art be more fully understood that the technical scheme in the application, below in conjunction with the accompanying drawing in the embodiment of the present application, technical scheme in the embodiment of the present application is clearly and completely described, obviously, described embodiment is only some embodiments of the present application, rather than whole embodiments. Based on the embodiment in the application, the every other embodiment that those of ordinary skill in the art obtain under not making creative work premise, all should belong to the scope of the application protection.

Below in conjunction with accompanying drawing, the method for acquisition three-phase relative permeability curve described herein is described in detail. Although this application provides the method operating procedure as described in following embodiment or flow chart, but based on conventional or more or less operating procedure can be included in the process without performing creative labour. Being absent from necessary causal step in logicality, the execution sequence of these steps is not limited to the execution sequence that the embodiment of the present application provides. When the device in practice of described method or end product perform, it is possible to perform or executed in parallel according to embodiment or method shown in the drawings order.

As it is shown in figure 1, the embodiment of the present application provides a kind of method obtaining three-phase relative permeability curve, the method includes:

S1: the air-water in based target region replaces immiscible displacement experiment, obtains not displacement pressure reduction in the same time, moisture content and gas-oil ratio, and described not displacement pressure reduction in the same time, moisture content and gas-oil ratio constitute dynamic data observation vector.

Real oil reservoir rock sample can be adopted, determine saturation end-point data when rock, physical properties of fluids parameter and two phase fluid flow by laboratory experiment, including: the porosity of rock, permeability, coefficient of compressibility; The biphase initial oil saturation of oil-water, the biphase residual oil saturation of oil-water, the critical gas saturation of vapor-liquid two phases, vapor-liquid two phases residual oil saturation etc. After determining above-mentioned data, can simulating oil deposit condition, carry out air-water for target area and replace non-mixed phase rock core displacement test, the such as note hydrocarbon gas or carbon dioxide displacement, and the experimental data such as displacement pressure reduction under real time record difference injection pore volume multiple (PV), oil displacement efficiency, moisture content and gas-oil ratio; Then from the experimental data recorded, obtain the not data such as displacement pressure reduction in the same time, moisture content and gas-oil ratio (ratio between crude oil amount and the volume of natural gas of extraction), and acquired not displacement pressure reduction in the same time, moisture content and gas-oil ratio are constituted dynamic data observation vector.

Described target area may refer to whole survey area, it is also possible to refers to the subregion in survey area.

S2: the priori three-phase relative permeability curve characterized based on constructed characterization model and constructed component numerical simulator, obtains current dynamic data predictive value vector.

Described characterization model may be used for characterizing the shape of the biphase permeability saturation curve of oil-water, the biphase permeability saturation curve of oil-gas and/or oil-gas-water three-phase relative permeability curve, and it can be cubic B-spline characterization model, but is not limited to this model.Its construction method is referred to correlation technique of the prior art, no longer goes to live in the household of one's in-laws on getting married at this and chats.

Described component numerical simulator can be based on PVT phase Characteristics analysis and minimum miscibility pressure is tested determined physical properties of fluids feature distribution parameter and built. Concrete,

PVT apparatus set and the combined unit of high pressure falling ball viscometer can be utilized, carry out single flash distillation, permanent matter expands, differential discharges and gas injection swell, measure gas injection molar fraction when taking different value, the Changing Pattern of saturation pressure when reservoir temperature, the relation between fluid pressure and volume, the Changing Pattern of oil density and viscosity.

Based on long slim tube driving test, when reservoir temperature and above bubble point pressure, the minimum miscibility pressure injecting gas can be measured. Under normal circumstances, it is possible to during using breakthrough of gas the degree of gathering reach 80% or ultimate recovery reach 90%��95% the two recovery ratio level as the standard judging displacement whether mixed phase.

Carry out PVT phase Characteristics analysis and minimum miscibility pressure test can be conducive to improving sign air-water and replace how non-phase-mixing driving oil tech improves the accuracy of recovery mechanism, be also beneficial to the concordance guaranteeing constructed component numerical simulator with actual experiment device.

Obtaining after above-mentioned physical properties of fluids feature distribution parameter, it is possible to build component numerical simulator based on saturation pressure, the Changing Pattern of oil density, the Changing Pattern of viscosity and the minimum miscibility pressure that measures. Described component numerical simulator could be for convection cell and carries out component numerical simulation. Described component numerical simulation is a kind of form of three-phase fluid numerical simulation, it can relate to gas phase-state change, oil phase density, viscosity and volume factor change etc., and it reaches to improve the effect of oil recovery factor mainly through reducing the difference of character between liquid phase, improve water-oil mobility ratio and improving oil displacement efficiency etc.

After building described component numerical simulator, it is possible to use the priori three-phase relative permeability curve that described characterization model characterizes obtains the dynamic data predictive value vector corresponding with described dynamic data observation vector. As in figure 2 it is shown, concrete can include following sub-step:

S21: obtain priori and control parameter vector set.

Described priori controls parameter vector set can include the priori control parameter vector corresponding to multiple relative permeability prior model, and each priori controls parameter vector and may be used for characterizing a priori three-phase relative permeability curve. Described prior model can be instant or stochastic generation in advance, and it corresponds to characterization model; Described priori three-phase relative permeability curve can be equiprobability random distribution.

S22: based on constructed component numerical simulator, utilizes acquired priori to control parameter vector set and carries out component numerical simulation, to obtain at the priori dynamic data predictive value vector corresponding to all relative permeability prior models of current time step.

After obtaining priori control parameter vector set, half iteration kalman filter method can be utilized, priori three-phase relative permeability curve corresponding to each prior model is carried out component numerical simulation, generates and walk the priori dynamic data predictive value vector corresponding to all relative permeability prior models in current time.

S23: calculate that described priori dynamic data predictive value is vectorial and difference between described dynamic data observation vector or error sum of squares, to obtain Corrective control parameter vector set.

After the priori dynamic data predictive value vector obtaining current time step, it is possible to ask for described priori and control the Kalman filtering factor of parameter vector set, then the described Kalman filtering factor, dynamic data observation vector described in described priori dynamic data predictive value vector assimilation are utilized. in one embodiment, dynamic data observation vector described in described priori dynamic data predictive value vector assimilation may refer to contrast vectorial for all of described priori dynamic data predictive value and described dynamic data observation vector, calculate that described priori dynamic data predictive value is vectorial and difference between described dynamic data observation vector or error sum of squares, to obtain difference between described dynamic data observation vector or the error sum of squares priori dynamic data predictive value vector in preset range, namely obtain meeting the priori dynamic data predictive value vector of preset requirement. described calculating described priori dynamic data predictive value is vectorial and error sum of squares between described dynamic data observation vector can include first calculating the difference between the dynamic data observation that in described priori dynamic data predictive value vector, each priori dynamic data predictive value is corresponding with described dynamic data observation vector, then obtained each difference is carried out a square calculating, finally square result of calculation of each difference is overlapped. described preset range can according to the algorithm used or set by person skilled.

Fig. 3-Fig. 4 illustrates the comparing result between the vector of priori dynamic data predictive value in actual applications and dynamic data observation vector, i.e. fitting result. Fig. 3 illustrates the fitting effect of displacement pressure reduction and moisture content; Fig. 4 illustrates the fitting effect of cumulative produced GOR. In this two width figure, f_wRepresent moisture content; �� p represents displacement pressure reduction; Rs represents gas-oil ratio; Experiment value corresponds to observation; Value of calculation corresponds to predictive value. All value of calculation matched with experiment value constitute the priori dynamic data predictive value vector meeting preset requirement.

After obtaining meeting the priori dynamic data predictive value vector of preset requirement, can picking out all prior models corresponding to priori dynamic data predictive value vector meeting preset requirement from all of relative permeability prior model, the priori corresponding to all relative permeability prior models picked out controls parameter vector and constitutes Corrective control parameter vector set.

S24: calculate the meansigma methods that the priori dynamic data predictive value corresponding to described Corrective control parameter vector set is vectorial, to obtain current dynamic data predictive value vector.

After obtaining Corrective control parameter vector set, it is possible to calculate the meansigma methods between all priori dynamic data predictive value vectors corresponding to described Corrective control parameter vector set, obtained meansigma methods is current dynamic data predictive value vector.

S3: utilize described dynamic data observation vector and described current dynamic data predictive value vector, be iterated constructed object function calculating, until the iterative computation of described object function meets default iteration convergence condition.

After obtaining current dynamic data predictive value vector, described dynamic data observation vector and described current dynamic data predictive value vector can be utilized, it is iterated the object function formerly built calculating, until the iterative computation of described object function meets default iteration convergence condition.

Described object function can be control the prior probability of each control parameter in parameter vector according to acquired dynamic data observation vector and described priori to build.Its expression formula can be expressed as follows:

$\begin{array}{c}O\left(m\right)=\frac{1}{2}{\left(g\right(m)-d_{obs})}^T{C}_D^{-1}\left(g\right(m)-d_{obs})\\ +\frac{1}{2}{(m-m_{prior})}^T{C}_m^{-1}(m-m_{prior})\end{array}---\left(1\right)$

Wherein, O (m) is object function; M is the control parameter vector in characterization model; T is the symbol characterizing vector or matrix transpose; d_obsFor dynamic data observation vector; G (m) is dynamic data predictive value vector, may refer to current dynamic data predictive value vector in the embodiment of the present application; C_DCovariance matrix for dynamic data measurement error; m_priorFor controlling the meansigma methods of the prior model information of parameter vector m; C_mCovariance matrix for prior model.

When described characterization model is cubic B-spline characterization model, the expression formula of described control parameter vector m can be expressed as follows:

$m=\[x_1^w,x_2^w,\mathrm{...},x_n^w,y_1^{ow},y_2^{ow},\mathrm{...},y_{n-1}^{ow},x_1^g,x_2^g,\mathrm{...},x_n^g,y_1^{og},y_2^{og},\mathrm{...},y_{n-1}^{og}\]---\left(2\right)$

Wherein,

$\left\{\begin{array}{c}{x}_1^u=\ln \left(\frac{C_i^u}{\frac{1}{2}(C_2^u+0)-C_1^u}\right)\\ x_i^u=\ln \left(\frac{C_i^u-(2C_{i-1}^u-C_{i-2}^u)}{\frac{1}{2}(C_{i+1}^u+C_{i-1}^u)-C_i^u}\right)\\ x_n^u=\ln \left(\frac{C_n^u-(2C_{n-1}^u-C_{n-2}^u)}{1-C_n^u}\right)\end{array}\right.,2\≤i\≤n-1;u=w,g---\left(3\right)$

$\left\{\begin{array}{c}{y}_1^v=\ln \left(\frac{C_1^v-(2C_2^v-C_3^v)}{\frac{1}{2}(C_2^v+1)-C_1^v}\right);\\ y_i^v=\ln \left(\frac{C_i^v-(2C_{i+1}^v-C_{i+2}^v)}{\frac{1}{2}(C_{i+1}^v+C_{i-1}^v)-C_i^v}\right),\\ y_{n-1}^v=\ln \left(\frac{C_{n-1}^v-0}{\frac{1}{2}{C}_{n-2}^v-C_{n-1}^v}\right).\end{array}\right.2\≤i\≤n-2;v=ow,og---\left(4\right)$

In upper facial (2)-Shi (4), C_iRepresent the control parameter in cubic B-spline characterization model; N is the number controlling node in cubic B-spline characterization model; W represents water; G represents gas; O represents oil; Ow represents oil-water; Og represents oil-gas.

Described be iterated object function calculating specifically concerning foreign affairs may refer to described control parameter vector is iterated calculate. The concrete grammar of iterative computation can be half iteration kalman filter method, but is not limited to the method, for instance can also be gauss-newton method.

In one embodiment, as it is shown in figure 5, this step concrete can include following sub-step:

S31: utilize described dynamic data observation vector and described current dynamic data predictive value vector, calculate the current value of described object function.

The control parameter vector that acquired described dynamic data observation vector, described current dynamic data predictive value vector and current iteration calculate can be updated in formula (1), even if the current value of described object function.

S32: judge that current iteration calculates and whether meet the iteration convergence condition preset.

After the current value obtaining described object function, it can be determined that current iteration calculates whether meet the iteration convergence condition preset. Described iteration convergence condition can be expressed as:

|O(m^k+1)-O(m^k) | < ��₁Or count > count_max(5)

In above formula, O (m^k+1) for the numerical value of object function of+1 iteration of kth, i.e. current value; O (m^k) for the numerical value of object function of kth time iteration, namely at first numerical value; ��₁For convergence precision, i.e. predetermined threshold value; Count is current iteration number of times; Count_maxFor maximum iteration time.

In one embodiment, can first contrasting the current value of described object function and described object function at first numerical value (numerical value that namely last time iterative computation is calculated), judge that whether difference therebetween is less than predetermined threshold value, when judging the current value of described object function and difference between first numerical value less than predetermined threshold value, stop iterative computation, using the current value of the described object function final numerical value as iterative computation; Then, when judging the current value of described object function and difference between first numerical value more than predetermined threshold value, may determine that whether current iteration number of times has reached set maximum iteration time, judging that current iteration number of times has reached set maximum iteration time, stop iterative computation, using the current value of the described object function final numerical value as iterative computation, when judging that current iteration number of times is not reaching to set maximum iteration time, continue to be iterated object function calculating.

In another embodiment, can first determine whether whether current iteration number of times has reached set maximum iteration time, judging that current iteration number of times has reached set maximum iteration time, stop iterative computation, using the current value of the described object function final numerical value as iterative computation; Then, when judging that current iteration number of times is not reaching to set maximum iteration time, contrasting the current value of described object function and described object function at first numerical value, judge that whether difference therebetween is less than predetermined threshold value, when judging the current value of described object function and difference between first numerical value less than or equal to predetermined threshold value, stop iterative computation, using the current value of the described object function final numerical value as iterative computation.

S33: when judging that current iteration calculating is unsatisfactory for iteration convergence condition, described Corrective control parameter vector set is controlled parameter vector set as priori, repeats step S22-S24, obtain current dynamic data predictive value vector;

When judging that current iteration calculating is unsatisfactory for iteration convergence condition, when namely needing to be iterated calculating, described Corrective control parameter vector set is controlled parameter vector set as priori, then repeats step S22-S24, obtain current dynamic data predictive value vector.

S34: after obtaining the current dynamic data predictive value vector corresponding to described Corrective control parameter vector set, repeats step S31-S32, until described iterative computation meets default iteration convergence condition, is met the object function of described iteration convergence condition.

S4: according to the object function meeting described iteration convergence condition, obtain the oil-gas-water three-phase relative permeability curve of target area.

When judging that current iteration meets default iteration convergence condition, the current value according to the object function that current iteration calculates, obtain the oil-gas-water three-phase relative permeability curve corresponding to target area. Concrete,

Can according to the current value of described object function, it is determined that go out each current value controlling parameter in described control parameter vector. Then, can according to current value controlling parameter each in described control parameter vector, calculating gas phase relative permeability and the oil relative permeability of the oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system, computing formula can be expressed as follows:

$k_{rp}\left(S_{pD}\right)=\underset{j=-3}{\overset{n-1}{\&Sigma;}}{C}_{j+2}^p{B}_{j,3}\left(S_{pD}\right)---\left(5\right)$

Wherein, k_rpRelative permeability for p phase fluid; P=w, ow, g, og; B_j,3(S_pD) for the B-spline basic function of quadravalence (3 times); $C_{-1}^p=2C_0^p-C_1^p$ And $C_{n+1}^p=2C_n^p-C_{n-1}^p.$

Finally, gas phase relative permeability and the oil relative permeability of the oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system can be utilized, calculating oil relative permeability when three phase fluid flow, its computing formula can be expressed as follows:

K_ro=(K_row+K_rw)(K_rog+K_rg)-(K_rw+K_rg)(6)

In above formula, K_roOil relative permeability when for three phase fluid flow; K_rowOil relative permeability for oil-aqueous two phase system; K_rwAqueous phase relative permeability for oil-aqueous two phase system; K_rogOil relative permeability for oil-gas binary system; K_rgGas phase relative permeability for oil-gas binary system.

Oil relative permeability when according to calculated three phase fluid flow, aqueous phase relative permeability and gas phase relative permeability, namely can obtain oil-gas-water three-phase relative permeability curve.

Fig. 6-Fig. 8 respectively illustrates the oil-gas-water three-phase relative permeability curve of a certain block in acquired NW Hebei. Wherein, Fig. 6 is oil relative permeability curve and the aqueous phase permeability saturation curve of oil-aqueous two phase system; Fig. 7 is oil relative permeability curve and the gas phase permeability saturation curve of oil-gas binary system; Fig. 8 is oil relative permeability curve when three phase fluid flow.

Can be seen that according to foregoing description, the not displacement pressure reduction in the same time that the embodiment of the present application gathers in immiscible displacement experiment by obtaining air-water in target area to replace, the observation of the dynamic data such as moisture content and gas-oil ratio and based on the acquired dynamic data predictive value of constructed component numerical simulator, then these data construct object functions are utilized, and be iterated calculating to constructed object function, it is met the object function of default iteration convergence condition, finally according to obtained object function, obtain the oil-gas-water three-phase relative permeability curve of target area, it is achieved thereby that provide the purpose being applicable to the oil-gas-water three-phase relative permeability curve that air-water replaces non-phase-mixing driving oil tech, this multiphase porous flow feature that can replace immiscible displacement for describing air-water in pore media provides theoretical foundation.

The embodiment of the present application additionally provides a kind of device obtaining three-phase relative permeability curve, as shown in Figure 9. This device includes: the first acquiring unit 410, second acquisition unit 420, iterative computation unit 430 and the 3rd acquiring unit 440. Wherein, first acquiring unit 410 may be used for the air-water in based target region and replaces immiscible displacement experiment, obtaining not displacement pressure reduction in the same time, moisture content and gas-oil ratio, described not displacement pressure reduction in the same time, moisture content and gas-oil ratio constitute dynamic data observation vector; Second acquisition unit 420 may be used for the priori three-phase relative permeability curve that characterizes based on constructed characterization model and constructed component numerical simulator, obtains current dynamic data predictive value vector; Iterative computation unit 430 may be used for utilizing described dynamic data observation vector and described current dynamic data predictive value vector, is iterated constructed object function calculating, until the iterative computation of described object function meets default iteration convergence condition; 3rd acquiring unit 440 may be used for, according to the object function meeting described iteration convergence condition, obtaining the oil-gas-water three-phase relative permeability curve of target area.

In at least one embodiment, second acquisition unit 420 can include (not shown):

Obtaining subelement, it is possible to be used for obtaining priori and control parameter vector set, described priori controls parameter vector set and includes the priori control parameter vector corresponding to multiple relative permeability prior model;

Component numerical value analog submodule unit, may be used for based on constructed component numerical simulator, utilize described priori to control parameter vector set and carry out component numerical simulation, to obtain the priori dynamic data predictive value vector corresponding to the plurality of relative permeability prior model that current time walks;

First computation subunit, it is possible to for the difference calculating that each described priori dynamic data predictive value is vectorial and between described dynamic data observation vector, to obtain Corrective control parameter vector set;

Second computation subunit, it is possible to for calculating the meansigma methods between the priori dynamic data predictive value vector corresponding to described Corrective control parameter vector set, to obtain current dynamic data predictive value vector,

Accordingly, iterative computation unit 430 may include that

3rd computation subunit, it is possible to be used for utilizing described dynamic data observation vector and described current dynamic data predictive value vector, calculate the current value of described object function;

Judgment sub-unit, it is possible to be used for judging current iteration calculates whether meet iteration convergence condition;

First controls subelement, may be used for when judging that current iteration calculating is unsatisfactory for iteration convergence condition, described Corrective control parameter vector set is controlled parameter vector set as priori, and control described component numerical value analog submodule unit, described first computation subunit and described second computation subunit operate accordingly, namely described component numerical value analog submodule unit is controlled based on constructed component numerical simulator, utilize described priori to control parameter vector set and carry out component numerical simulation, to obtain the priori dynamic data predictive value vector corresponding to the plurality of relative permeability prior model that current time walks, control described first computation subunit and calculate that each described priori dynamic data predictive value is vectorial and difference between described dynamic data observation vector, to obtain Corrective control parameter vector set, and control the meansigma methods that described second computation subunit calculating priori dynamic data predictive value corresponding to described Corrective control parameter vector set is vectorial, to obtain the operations such as current dynamic data predictive value vector,

Second controls subelement, for controlling the operation of described 3rd computation subunit and described judgment sub-unit, concrete, utilize described dynamic data observation vector and described current dynamic data predictive value vector to calculate the current value of described object function for controlling the 3rd computation subunit, and control judgment sub-unit judges current iteration calculates whether meet iteration convergence condition, until being met the object function of iteration convergence condition.

In at least one embodiment, the 3rd acquiring unit 440 can include (not shown):

Determine subelement, described in basis, meet the object function of iteration convergence condition, it is determined that go out each current value controlling parameter in described control parameter vector;

4th computation subunit, for according to current value controlling parameter each in described control parameter vector, calculating gas phase relative permeability and the oil relative permeability of the oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system;

5th computation subunit, for utilizing gas phase relative permeability and the oil relative permeability of the computed oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system, calculate oil relative permeability when three phase fluid flow.

System, device or the unit that above-described embodiment illustrates, specifically can be realized by computer chip or entity, or be realized by the product with certain function.

For convenience of description, it is divided into various unit to be respectively described with function when describing apparatus above. Certainly, the function of each unit can be realized in same or multiple softwares and/or hardware when implementing the application.

Method described in the embodiment of the present invention or the step of algorithm can be directly embedded into hardware, processor performs software module or the combination of both. In one or more exemplary designs, the above-mentioned functions described by the embodiment of the present invention can realize in the combination in any of hardware, software, firmware or this three.

Each embodiment in this specification all adopts the mode gone forward one by one to describe, between each embodiment identical similar part mutually referring to, what each embodiment stressed is the difference with other embodiments. Especially for system embodiment, owing to it is substantially similar to embodiment of the method, so what describe is fairly simple, relevant part illustrates referring to the part of embodiment of the method.

Although depicting the application by embodiment, it will be appreciated by the skilled addressee that the application has many deformation and is varied without departing from spirit herein, it is desirable to appended claim includes these deformation and is varied without departing from spirit herein.

Claims (10)

1. the method obtaining three-phase relative permeability curve, it is characterised in that including:

S1, the air-water in based target region replaces immiscible displacement experiment, obtains not displacement pressure reduction in the same time, moisture content and gas-oil ratio, and described not displacement pressure reduction in the same time, moisture content and gas-oil ratio constitute dynamic data observation vector;

S2, the priori three-phase relative permeability curve characterized based on constructed characterization model and constructed component numerical simulator, obtain current dynamic data predictive value vector;

S3, utilizes described dynamic data observation vector and described current dynamic data predictive value vector, is iterated constructed object function calculating, until the iterative computation of described object function meets default iteration convergence condition;

S4, according to the object function meeting described iteration convergence condition, obtains the oil-gas-water three-phase relative permeability curve of described target area.

2. method according to claim 1, it is characterized in that, described component numerical simulator builds based on determined physical properties of fluids feature distribution parameter, and described physical properties of fluids parameter attribute includes the Changing Pattern of saturation pressure, oil density, viscosity and volume factor.

3. method according to claim 1, it is characterised in that described step S2 includes:

S21, obtains priori and controls parameter vector set, and described priori controls parameter vector set and includes the priori control parameter vector corresponding to multiple relative permeability prior model;

S22, based on constructed component numerical simulator, utilizes described priori to control parameter vector set and carries out component numerical simulation, to obtain the priori dynamic data predictive value vector corresponding to the plurality of relative permeability prior model that current time walks;

S23, calculates that each described priori dynamic data predictive value is vectorial and difference between described dynamic data observation vector or error sum of squares, to obtain meeting the Corrective control parameter vector set of preset requirement;

S24, calculates the meansigma methods that the priori dynamic data predictive value corresponding to described Corrective control parameter vector set is vectorial, to obtain current dynamic data predictive value vector,

Accordingly, described step S3 includes:

S31, utilizes described dynamic data observation vector and described current dynamic data predictive value vector, calculates the current value of described object function;

S32, it is judged that current iteration calculates whether meet iteration convergence condition;

S33, when judging that current iteration calculating is unsatisfactory for iteration convergence condition, controls parameter vector set using described Corrective control parameter vector set as priori, repeats step S22-S24, obtain current dynamic data predictive value vector;

S34, after obtaining the current dynamic data predictive value vector corresponding to described Corrective control parameter vector set, repeats step S31-32, until being met the object function of described iteration convergence condition.

4. the method according to claim 1 or 3, it is characterised in that the expression formula of constructed object function is as follows:

$\begin{array}{c}O\left(m\right)=\frac{1}{2}{\left(g\right(m)-d_{obs})}^T{C}_D^{-1}\left(g\right(m)-d_{obs})\\ +\frac{1}{2}{(m-m_{prior})}^T{C}_m^{-1}(m-m_{prior})\end{array}$

Wherein, O (m) is object function; M is the control parameter vector in described characterization model; T is the symbol characterizing vector or matrix transpose; d_obsFor dynamic data observation vector; G (m) is dynamic data predictive value vector; C_DCovariance matrix for dynamic data measurement error; m_priorMeansigma methods for the prior model information of described control parameter vector m; C_mCovariance matrix for prior model.

5. method according to claim 4, it is characterised in that described basis meets the object function of described iteration convergence condition, the oil-gas-water three-phase relative permeability curve obtaining described target area includes:

According to the object function meeting described iteration convergence condition, it is determined that go out each current value controlling parameter in described control parameter vector;

According to current value controlling parameter each in described control parameter vector, calculate gas phase relative permeability and the oil relative permeability of the oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system;

Utilize gas phase relative permeability and the oil relative permeability of the computed oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system, calculate oil relative permeability when three phase fluid flow.

6. method according to claim 5, it is characterised in that calculate oil relative permeability when three phase fluid flow by below equation:

K_ro=(K_row+K_rw)(K_rog+K_rg)-(K_rw+K_rg)

In formula, K_roOil relative permeability when for three phase fluid flow; K_rowOil relative permeability for oil-aqueous two phase system; K_rwAqueous phase relative permeability for oil-aqueous two phase system; K_rogOil relative permeability for oil-gas binary system;K_rgGas phase relative permeability for oil-gas binary system.

7. the device obtaining three-phase relative permeability curve, it is characterised in that including:

First acquiring unit, air-water for based target region replaces immiscible displacement experiment, obtaining not displacement pressure reduction in the same time, moisture content and gas-oil ratio, described not displacement pressure reduction in the same time, moisture content and gas-oil ratio constitute dynamic data observation vector;

Second acquisition unit, for the priori three-phase relative permeability curve characterized based on constructed characterization model and constructed component numerical simulator, obtains current dynamic data predictive value vector;

Iterative computation unit, is used for utilizing described dynamic data observation vector and described current dynamic data predictive value vector, is iterated constructed object function calculating, until the iterative computation of described object function meets default iteration convergence condition;

3rd acquiring unit, for according to the object function meeting described iteration convergence condition, obtaining the oil-gas-water three-phase relative permeability curve of described target area.

8. device according to claim 7, it is characterised in that described second acquisition unit includes:

Obtaining subelement, be used for obtaining priori and control parameter vector set, described priori controls parameter vector set and includes the priori control parameter vector corresponding to multiple relative permeability prior model;

Component numerical value analog submodule unit, for based on constructed component numerical simulator, utilize described priori to control parameter vector set and carry out component numerical simulation, to obtain the priori dynamic data predictive value vector corresponding to the plurality of relative permeability prior model that current time walks;

First computation subunit, for calculating that each described priori dynamic data predictive value is vectorial and difference between described dynamic data observation vector or error sum of squares, to obtain Corrective control parameter vector set;

Second computation subunit, for calculating the meansigma methods of the dynamic data predictive value vector corresponding to described Corrective control parameter vector set, to obtain current dynamic data predictive value vector,

Accordingly, described iterative computation unit includes:

3rd computation subunit, is used for utilizing described dynamic data observation vector and described current dynamic data predictive value vector, calculates the current value of described object function;

Judgment sub-unit, is used for judging current iteration calculates whether meet iteration convergence condition;

First controls subelement, for when judging that current iteration calculating is unsatisfactory for iteration convergence condition, described Corrective control parameter vector set is controlled parameter vector set as priori, and controls described component numerical value analog submodule unit, described first computation subunit and described second computation subunit and operate accordingly;

Second controls subelement, for controlling the operation of described 3rd computation subunit and described judgment sub-unit, until being met the object function of iteration convergence condition.

9. the device according to claim 7 or 8, it is characterised in that the expression formula of object function constructed in described iterative computation unit is as follows:

$\begin{array}{c}O\left(m\right)=\frac{1}{2}{\left(g\right(m)-d_{obs})}^T{C}_D^{-1}\left(g\right(m)-d_{obs})\\ +\frac{1}{2}{(m-m_{prior})}^T{C}_m^{-1}(m-m_{prior})\end{array}$

Wherein, O (m) is object function; M is the control parameter vector in described characterization model; T is the symbol characterizing vector or matrix transpose; d_obsFor dynamic data observation vector; G (m) is dynamic data predictive value vector; C_DCovariance matrix for dynamic data measurement error;M_priorMeansigma methods for the prior model information of described control parameter vector m; C_mCovariance matrix for prior model.

10. device according to claim 9, it is characterised in that described 3rd acquiring unit includes:

Determine subelement, described in basis, meet the object function of iteration convergence condition, it is determined that go out each current value controlling parameter in described control parameter vector;

4th computation subunit, for according to current value controlling parameter each in described control parameter vector, calculating gas phase relative permeability and the oil relative permeability of the oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system;

5th computation subunit, for utilizing gas phase relative permeability and the oil relative permeability of the computed oil relative permeability of oil-aqueous two phase system, aqueous phase relative permeability and oil-gas binary system, calculate oil relative permeability when three phase fluid flow.